DBMS > EsgynDB vs. Google BigQuery vs. Manticore Search vs. Percona Server for MongoDB

System Properties Comparison EsgynDB vs. Google BigQuery vs. Manticore Search vs. Percona Server for MongoDB

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Large scale data warehouse service with append-only tables

Multi-storage database for search, including full-text search.

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Primary database model

Relational DBMS

Search engine

Document store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

www.esgyn.cn

cloud.google.com/bigquery

manticoresearch.com

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

cloud.google.com/bigquery/docs

manual.manticoresearch.com

docs.percona.com/percona-distribution-for-mongodb

Developer

Esgyn

Google

Manticore Software

Percona

Initial release

2015

2010

2017

2015

Current release

6.0, February 2023

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

GPL version 2

Open Source

GPL Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

Server operating systems

Linux

hosted

FreeBSD
Linux
macOS
Windows

Linux

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

user defined functions

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Synchronous replication based on Galera library

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Since BigQuery is designed for querying data

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

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